An efficient two-stage Markov chain Monte Carlo method for dynamic data integration

Yalchin Efendiev*, A. Datta-Gupta, V. Ginting, X. Ma, B. Mallick

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

88 Scopus citations

Abstract

[1] In this paper, we use a two-stage Markov chain Monte Carlo (MCMC) method for subsurface characterization that employs coarse-scale models. The purpose of the proposed method is to increase the acceptance rate of MCMC by using inexpensive coarse-scale runs based on single-phase upscaling. Numerical results demonstrate that our approach leads to a severalfold increase in the acceptance rate and provides a practical approach to uncertainty quantification during subsurface characterization.

Original languageEnglish (US)
Article numberW12423
Pages (from-to)1-6
Number of pages6
JournalWater Resources Research
Volume41
Issue number12
DOIs
StatePublished - Dec 1 2005

ASJC Scopus subject areas

  • Water Science and Technology

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